Publications (8)
Differentiating Through a Quadratic Cone Program
Quill Healey, Parth Nobel, Stephen Boyd
Quadratic cone programs are rapidly becoming the standard canonical form for convex optimization problems. In this paper we address the question of differentiating the solution map…
Disciplined Nonlinear Programming
Daniel Cederberg, William Zhang, Parth Nobel +1
We introduce disciplined nonlinear programming (DNLP), a syntax for specifying nonlinear programming problems. DNLP is inspired by disciplined convex programming (DCP) and allows s…
RandALO: Out-of-sample risk estimation in no time flat
Parth Nobel, Daniel LeJeune, Emmanuel J. Candès
Estimating out-of-sample risk for models trained on large high-dimensional datasets is an expensive but essential part of the machine learning process, enabling practitioners to op…
CuClarabel: GPU Acceleration for a Conic Optimization Solver
Yuwen Chen, Danny Tse, Parth Nobel +2
We present the GPU implementation of the general-purpose interior-point solver Clarabel for convex optimization problems with conic constraints. We introduce a mixed parallel compu…
Fast Path Planning Through Large Collections of Safe Boxes
Tobia Marcucci, Parth Nobel, Russ Tedrake +1
We present a fast algorithm for the design of smooth paths (or trajectories) that are constrained to lie in a collection of axis-aligned boxes. We consider the case where the numbe…
Tractable Evaluation of Stein's Unbiased Risk Estimate with Convex Regularizers
Parth Nobel, Emmanuel Candès, Stephen Boyd
Stein's unbiased risk estimate (SURE) gives an unbiased estimate of the risk of any estimator of the mean of a Gaussian random vector. We focus here on the case when the e…
CVXPY 1.9: Recent Advances in Optimization Modeling Software
William Zhang, Parth Nobel, Aryaman Jeendgar +3
CVXPY is a Python-embedded domain-specific language for convex optimization that lets users express problems in mathematical notation while the system verifies convexity and reduce…
Computing Tighter Bounds on the -Queens Constant via Newton's Method
Parth Nobel, Akshay Agrawal, Stephen Boyd
In recent work Simkin shows that bounds on an exponent occurring in the famous -queens problem can be evaluated by solving convex optimization problems, allowing him to find bou…